roast {limma}R Documentation

Rotation Gene Set Tests

Description

Rotation gene set testing for linear models.

Usage

roast(iset=NULL, y, design, contrast=ncol(design), set.statistic="mean",
     gene.weights=NULL, array.weights=NULL, block=NULL, correlation,
     var.prior=NULL, df.prior=NULL, trend.var=FALSE, nrot=999)
mroast(iset=NULL, y, design, contrast=ncol(design), set.statistic="mean",
     gene.weights=NULL, array.weights=NULL, block=NULL, correlation,
     var.prior=NULL, df.prior=NULL, trend.var=FALSE, nrot=999, adjust.method="BH", midp=TRUE)

Arguments

iset

index vector specifying which rows (probes) of y are in the test set. This can be a vector of indices, or a logical vector of the same length as statistics, or any vector such as y[iset,] contains the values for the gene set to be tested. For mroast, iset is a list of index vectors.

y

numeric matrix giving log-expression or log-ratio values for a series of microarrays, or any object that can coerced to a matrix including ExpressionSet, MAList, EList or PLMSet objects. Rows correspond to probes and columns to samples. If either var.prior or df.prior are null, then y should contain values for all genes on the arrays. If both prior parameters are given, then only y values for the test set are required.

design

design matrix

contrast

contrast for which the test is required. Can be an integer specifying a column of design, or else a contrast vector of length equal to the number of columns of design.

set.statistic

summary set statistic. Possibilities are "mean","floormean","mean50" or "msq".

gene.weights

optional numeric vector of weights for genes in the set. Can be positive or negative. For mroast this vector must have length equal to nrow(y). For roast, can be of length nrow(y) or of length equal to the number of genes in the test set.

array.weights

optional numeric vector of array weights.

block

optional vector of blocks.

correlation

correlation between blocks.

var.prior

prior value for residual variances. If not provided, this is estimated from all the data using squeezeVar.

df.prior

prior degrees of freedom for residual variances. If not provided, this is estimated using squeezeVar.

trend.var

logical, should a trend be estimated for var.prior? See eBayes for details. Only used if var.prior or df.prior are NULL.

nrot

number of rotations used to estimate the p-values.

adjust.method

method used to adjust the p-values for multiple testing. See p.adjust for possible values.

midp

logical, should mid-p-values be used in instead of ordinary p-values when adjusting for multiple testing?

Details

This function implements the ROAST gene set test from Wu et al (2010). It tests whether any of the genes in the set are differentially expressed. The function can be used for any microarray experiment which can be represented by a linear model. The design matrix for the experiment is specified as for the lmFit function, and the contrast of interest is specified as for the contrasts.fit function. This allows users to focus on differential expression for any coefficient or contrast in a linear model. If contrast is not specified, the last coefficient in the linear model will be tested. The arguments array.weights, block and correlation have the same meaning as they for for the lmFit function.

The arguments df.prior and var.prior have the same meaning as in the output of the eBayes function. If these arguments are not supplied, they are estimated exactly as is done by eBayes.

The argument gene.weights allows directions or weights to be set for individual genes in the set.

The gene set statistics "mean", "floormean", "mean50" and msq are defined by Wu et al (2010). The different gene set statistics have different sensitivities to small number of genes. If set.statistic="mean" then the set will be statistically significantly only when the majority of the genes are differentially expressed. "floormean" and "mean50" will detect as few as 25% differentially expressed. "msq" is sensitive to even smaller proportions of differentially expressed genes, if the effects are reasonably large.

The output gives p-values three possible alternative hypotheses, "Up" to test whether the genes in the set tend to be up-regulated, with positive t-statistics, "Down" to test whether the genes in the set tend to be down-regulated, with negative t-statistics, and "Mixed" to test whether the genes in the set tend to be differentially expressed, without regard for direction.

roast estimates p-values by simulation, specifically by random rotations of the orthogonalized residuals (Langsrud, 2005), so p-values will vary slightly from run to run. To get more precise p-values, increase the number of rotations nrot. The p-value is computed as (b+1)/(nrot+1) where b is the number of rotations giving a more extreme statistic than that observed (Phipson and Smyth, 2010). This means that the smallest possible p-value is 1/(nrot+1).

mroast does roast tests for multiple sets, including adjustment for multiple testing. By default, mroast reports ordinary p-values but uses mid-p-values at the multiple testing stage. Mid-p-values are probably a good choice when using false discovery rates (adjust.method="BH") but not when controlling the family-wise type I error rate (adjust.method="holm").

Value

roast produces an object of class "Roast". This consists of a list with the following components:

p.value

data.frame with columns Active.Prop and P.Value, giving the proportion of genes in the set contributing meaningfully to significance and estimated p-values, respectively. Rows correspond to the alternative hypotheses mixed, up or down.

var.prior

prior value for residual variances.

df.prior

prior degrees of freedom for residual variances.

mroast produces a list of three matrices, each with a row for each set:

P.Value

unadjusted p-values for the mixed, up and down alternative hypotheses

Adj.P.Value

adjusted p-values for each set and each hypothesis

Active.Proportion

proportion of active genes for each set and each hypothesis

Note

The default setting for the set statistic was changed in limma 3.5.9 (3 June 2010) from "msq" to "mean".

Author(s)

Gordon Smyth and Di Wu

References

Goeman, JJ, and Buhlmann, P (2007). Analyzing gene expression data in terms of gene sets: methodological issues. Bioinformatics 23, 980-987.

Langsrud, O (2005). Rotation tests. Statistics and Computing 15, 53-60.

Phipson B, and Smyth GK (2010). Permutation P-values should never be zero: calculating exact P-values when permutations are randomly drawn. Statistical Applications in Genetics and Molecular Biology, Volume 9, Article 39.

Routledge, RD (1994). Practicing safe statistics with the mid-p. Canadian Journal of Statistics 22, 103-110.

Wu, D, Lim, E, Francois Vaillant, F, Asselin-Labat, M-L, Visvader, JE, and Smyth, GK (2010). ROAST: rotation gene set tests for complex microarray experiments. Bioinformatics 26, 2176-2182. http://bioinformatics.oxfordjournals.org/cgi/content/abstract/btq401?

See Also

roast performs a self-contained test in the sense defined by Goeman and Buhlmann (2007). For a competitive gene set test, see wilcoxGST. For a competitive gene set enrichment analysis using a database of gene sets, see romer.

An overview of tests in limma is given in 08.Tests.

Examples

y <- matrix(rnorm(100*4),100,4)
design <- cbind(Intercept=1,Group=c(0,0,1,1))

# First set of 5 genes contains 3 that are genuinely differentially expressed
iset1 <- 1:5
y[iset1,3:4] <- y[iset1,3:4]+3

# Second set of 5 genes contains none that are DE
iset2 <- 6:10

roast(iset1,y,design,contrast=2)
mroast(list(set1=iset1,set2=iset2),y,design,contrast=2)

[Package limma version 3.10.2 Index]